Leffingwell Odor Dataset

<strong>NOTE: It's easier to download this dataset from pyrfume. Here's how:</strong> <pre><code># First install pyrfume in your Python environment. This can be done easily with pip. # pip install pyrfume import pyrfume molecules = pyrfume.load_data('leffingwell/molecules.csv', remote=True) behavior = pyrfume.load_data('leffingwell/behavior.csv', remote=True) # e.g. to count the number of molecules with each descriptor behavior.sum().sort_values(ascending=False).astype(int) </code></pre> Predicting properties of molecules is an area of growing research in machine learning, particularly as models for learning from graph-valued inputs improve in sophistication and robustness. A molecular property prediction problem that has received comparatively little attention during this surge in research activity is building Structure-Odor Relationships (SOR) models (as opposed to Quantitative Structure-Activity Relationships, a term from medicinal chemistry). This is a 70+ year-old problem straddling chemistry, physics, neuroscience, and machine learning. To spur development on the SOR problem, we curated and cleaned a dataset of 3523 molecules associated with expert-labeled odor descriptors from the <em>Leffingwell PMP 2001</em> database. We provide featurizations of all molecules in the dataset using bit-based and count-based fingerprints, Mordred molecular descriptors, and the embeddings from our trained GNN model (Sanchez-Lengeling et al., 2019). This dataset is comprised of two files: <strong>leffingwell_data.csv</strong>: this contains molecular structures, and what they smell like, along with train, test, and cross-validation splits. More detail on the file structure is found in leffingwell_readme.pdf. <strong>leffingwell_embeddings.npz</strong>: this contains several featurizations of the molecules in the dataset. <strong>leffingwell_readme.pdf</strong>: a more detailed description of the data and its provenance, including expected performance metrics. <strong>LICENSE</strong>: a copy of the CC-BY-NC license language. The dataset, and all associated features, is freely available for research use under the CC-BY-NC license. If you use the data in a publication, please cite: <pre>@article{sanchez2019machine, title={Machine learning for scent: Learning generalizable perceptual representations of small molecules}, author={Sanchez-Lengeling, Benjamin and Wei, Jennifer N and Lee, Brian K and Gerkin, Richard C and Aspuru-Guzik, Al{\'a}n and Wiltschko, Alexander B}, journal={arXiv preprint arXiv:1910.10685}, year={2019} }</pre>

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